In the past, a typical application for copiers or scan-to-print image processing systems was to reproduce an input image as accurately as possible, i.e., render a copy. Thus, copies have been rendered as accurately as possible, flaws and all. However, as customers become more knowledgeable in their document reproduction requirements, they recognize that an exact copy is often not what they want. Instead, they would rather obtain the best possible document output. Until recently, image quality from the output of a copier or a scan-to-print system was directly related to the input document quality. One very common set of input documents includes photographs. Unfortunately, photography is an inexact science, particularly among amateurs, and original photographs are often poor. Alternately, technology, age or image degradation variations often result in pictures having an unsatisfactory and undesirable appearance. What is desired then, is a copy giving the best possible picture, and not a copy of the original.
Photography has long dealt with this issue. Analog filters and illumination variations can improve the appearance of pictures in the analog photographic process. Thus, for example, yellow filters enhance the appearance of white clouds against a blue sky in black and white images. Further, various electrophotographic devices, including digital copiers, can clean up and improve images by adjustment of threshold, filtering, or background suppression. Generally, these methods are manual methods which a user must select on an image by image basis. Unfortunately, the casual user is not skilled enough to perform these operations. The inability to perform image enhancement operations is exacerbated when additionally dealing with color controls.
In digital image processing, three possible choices are presented by the art in the area of image enhancement. In the first case, we can do nothing. Such a system is a stable system, in that it does no harm to an image, but its output documents are sometimes not satisfactory to the ultimate customer.
In a second case of image enhancement, the image can always be processed. It turns out than an improvement can usually be made to an image if certain assumptions are made that are accurate for most cases. In an exceptionally large set of image, increasing contrast, sharpness, and/or color saturation, will improve the image. This model tends to produce better images, but the process is unstable, in that for multi-generation copying, increases in contrast, saturation, or sharpness are undesirable and ultimately lead to a severe image degradation. Further, the process may undesirably operate on those images which are good ones.
Accordingly, we arrive at our third case of image enhancement, a process of automated image enhancement which operates to vary images which are not perceived as good images, but does not operate on images which do not need to be improved, thereby allowing a stable process.
One improvement that can be made to an image is correction of color shifts. When photographic color prints (a significant image source for electronic images) are made from negative or positive color images, the overall colors of the printer frequently do not correspond to those of the subject photographed. This may arise from a number of causes, such as change in sensitivity of the film due to aging, use of incorrect lighting, error in print processing and the like. If an exact print is made from such pictures based on the sensitivity which the film should have had, i.e., assuming that the positive or negative was correct, then the printer, particularly if a reflection prints (as opposed to a transparency) will display the errors to a greater extent than the transparency because such printers are nearly always viewed under conditions in which there are comparison objects. Of course, electronic images can only reproduce what is recorded, but given the possibility of pixel by pixel color editing.
U.S. Pat. No. 2,571,697 to Evans (hereinafter, Evan's Theorem) teaches that in photographic processes, an overall color shift can be made to the image without knowledge of the original colors. Initially, an assumption is made that if light passes though the printing apparatus onto a printing material without a color image or other obstruction in the light beam, the printing light or imaging illumination should produce a neutral gray (approximately). For a color shifted image, if light which will reproduce substantially as gray on the printing material is permitted to pass though the transparency so that a uniform amount of the light strikes the transparency at all points, the light passing through the transparency usually will not print as neutral gray, but will deviate from gray by an appreciable amount. The light which passes through the transparency is collected or integrated and each component primary color forming the light is measured by the use of a photoelectric cell. By comparing the amount of these colors received after passing through the transparency with the amount of these colors in light from the light source, a correction factor can be determined, and an adjusted light source can be provided, by inserting a filter in the light path. While the Evans Theorem works for a class of natural scene images, it fails in images which have unusual color usage, and particularly where a single color predominates the picture.
"Signal Processing by the Input Interface to a Digital Color Laser Copier", by A. Usami, SID 90 Digest, p. 498-500 (1990) compares Evan's Theorem with a color balance method which attempts to make the whitest points in each of the red, green and blue image signals equal to one another to correct color in an image. It is clear that the color of any object within the image is a function of the object's actual color and the light with which it is imaged, and accordingly that color corrections can be made if the illumination with which the object was imaged is known. An assumption is made that in almost any image, there is an almost specularly reflecting object that, when imaged, reflects the imaging illumination. If that object can be found (and ignoring saturated pixels) the light reflected from that object will closely match the imaging illumination. If the image is defined in term of r, g, b, color space, then EQU W.sub.max =max(R.sub.max, G.sub.max, B.sub.max).
where
Accordingly, a gain function .sub.Y can be derived for each color where EQU .sub.Y WR=W.sub.max/ R.sub.max EQU .sub.Y WG=W.sub.max/ G.sub.max EQU .sub.Y WB=W.sub.max/ B.sub.max
Clearly, where color is correct, or close to correct, little change is made within the image. However, the described gain function has a tendency to only correct highlight areas in the image. Color shifts in shadow areas remain.
References cited herein are incorporated by reference for their teachings.